Typical applications of on-line analytical processing (OLAP) system require the fast access of multidimensional data. Data warehouse implemented in the OLAP system is a central database used for storing important data collected by various business systems belonging to a company. The central database of the data warehouse generally connects to a main server in the OLAP system.
The OLAP system enables users to acquire data in various dimensions easily and selectively and to review a table established according to the acquired data. For example, when data analysis is requested, the OLAP system provides a trail balance presenting sales information associated with all products sold in a certain electronic company for comparison. For instance, the sales information includes the profit data of a mobile phone on a current month and a previous month and the sales volumes of various mobile phones in the same period.
For a conventional OLAP system, it requires data warehouse tools to generate a statement for the aforementioned comparison results. The data warehouse tools organize data values in a form in a relational database into a cube having a multidimensional structure, and then generate a pivot analysis table according to the dimensionality, attributes and structure of cube.
However, such a pivot analysis table only shows attributes of the selected data values for comparison, where the conventional OLAP system have not processed the dimensionality, attributes and structure of the selected data values. To process the dimensionality, attributes and structure of data values additionally, extra data tables are required. After the dimensionality, attributes and structure of data values are processed, the conventional OLAP system outputs another pivot analysis table according to the processed dimensionality, attributes and structure of data values. This procedure is very complicated, requires more storage space and has errors easily.
Therefore, a more efficient pivot analysis method is required in the art to provide a fast way between data processing and data presentation and to present computing relationships and comparison relationships among data values.